Geological Hazard Susceptibility Analysis and Developmental Characteristics Based on Slope Unit, Using the Xinxian County, Henan Province as an Example.

Geological hazards in Xinxian County, Xinyang City, Henan Province, are characterized by their small scale, wide distribution, and significant influence from regional tectonics. This study focuses on collapses and landslide hazards within the area, selecting twelve evaluation factors: aspect, slope shape, normalized difference vegetation index (NDVI), topographic relief, distance from geological structure, slope, distance from roads, land use cover type, area of land change (2012-2022), average annual rainfall (2012-2022), and river network density. Utilizing data from historical disaster sites across the region, the information quantity method and hierarchical analysis method are employed to ascertain the information quantity and weight of each factor. Subsequently, a random forest model is applied to perform susceptibility zoning of geological hazards in Xinxian County and to examine the characteristics of these geological disasters. The results show that in the study area, the primary factors influencing the development of geohazards are the distance from roads, rock groups, and distance from geological structure areas. A comparison of the susceptibility results obtained through two methods, the analytic hierarchy process information quantity method and the random forests model, reveals that the former exhibits a higher accuracy. This model categorizes the geohazard susceptibility in the study area into four levels: low, medium, high, and very high. Notably, the areas of very high and high susceptibility together cover 559.17 km2, constituting 35.99% of the study area's total area, and encompass 57 disaster sites, which represent 72.15% of all disaster sites. Geological hazards in Xinxian County frequently manifest on steep canyon inclines, along the curved and concave banks of mountain rivers, within watershed regions, on gully inclines, atop steep cliffs, and on artificially created slopes, among other sites. Areas with very high and high vulnerability to these hazards are mainly concentrated near the county's geological formations. The gneiss formations are widely exposed in Xinxian County, and the gneisses' strength is significantly changed under weathering, which makes the properties of the different degrees of weathering of the rock and soil bodies play a decisive role in the stability of the slopes. This paper provides a basis for evaluating and preventing geologic hazards in the Dabie mountainous area of the South Henan Province, and the spatial planning of the national territory.

Estimate of soil heavy metal in a mining region using PCC-SVM-RFECV-AdaBoost combined with reflectance spectroscopy.

Soil contamination with heavy metals is a relatively serious issue in China. Traditional soil heavy metal survey methods cannot meet the demand for rapid and real-time large-scale area soil heavy metal surveys. We chose a typical mining area in Henan Province as the study area, collected 124 soil samples in the field and obtained their soil hyperspectral data indoors using a spectrometer. After different spectral transformations of the soil spectral curves, Pearson correlation coefficients (PCC) between them and the heavy metals Cd, Cr, Cu, and Ni were calculated, and after correlation evaluation, the best spectral transformations for each heavy metal were determined and preselected characteristic wavebands were obtained. Then the support vector machine recursive feature elimination cross-validation (SVM-RFECV) is used to select among the preselected feature wavebands to obtain the final modeled wavebands, and the Adaptive Boosting (AdaBoost), Gradient Boosting Decision Tree (GBDT), Random Forest (RF), and Partial Least Squares (PLS) methods were used to establish the inversion model. The results showed that the PCC-SVM-RFECV can effectively select characteristic wavebands with high contribution to modeling from high-dimensional data. Spectral transformations methods can improve the correlation of spectra with heavy metals. The location and quantity of characteristic wavebands for the four heavy metals were different. The accuracy of AdaBoost was significantly better than that of GBDT, RF, and PLS (i.e., Ni: [Formula: see text]). This study can provide a technical reference for the use of hyperspectral inversion models for large-scale monitoring of soil heavy metal content.

Iron-modulated Ni3S2 derived from a Ni-MOF-based Prussian blue analogue for a highly efficient oxygen evolution reaction.

Developing efficient, environmentally friendly and cost-effective non-precious metal electrocatalysts for the oxygen evolution reaction (OER) is essential to alleviate the energy crisis and environmental pollution. Herein, we report a simple and practical method to prepare non-precious metal catalysts, namely iron-modulated Ni3S2 (Fe-Ni3S2/NF) on nickel foam, by growing a Ni-MOF directly on 3D porous conductive nickel foam, followed by the formation of Ni-MOF-based Prussian blue analogs (Ni-MOF@PBA) via in situ cation exchange reactions, which are further sulfidated to iron-modulated Ni3S2. Based on a series of characterization results, it is confirmed that iron acts as a modulator at the Ni active site, leading to electron depletion, thereby modulating the electron spin state and optimizing the binding energy of key reaction intermediates, resulting in highly exposed active sites and acceleration of OER reaction kinetics. The synthesized Fe-Ni3S2/NF exhibits excellent activity in alkaline media, which needs overpotentials of only 232 mV and 287 mV to drive current densities of 10 mA cm-2 and 50 mA cm-2, respectively. Additionally, Fe-Ni3S2/NF exhibits excellent stability for at least 24 h during the OER process. This work presents a rational design and synthesis of transition metal-based catalysts with nanocone structures, providing a new strategy for assembling advanced materials and insights for exploring various energy storage and conversion systems.

Detecting ecological spatial-temporal changes by Remote Sensing Ecological Index with local adaptability.

Ecological environmental assessment is an indispensable part of the eco-environment protection system. As researchers have increasingly focused on ecological environment protection, the ecological environment evaluation system has been gradually improved. The enhancement of the ecological environment evaluation system provides more scientific and effective data support for ecological environment monitoring and governance. This article examines the Wuhan Urban Development Zone as an example, selects Landsat 8 (Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS)) images of the study area from 2013 to 2019 at two-year intervals, and applies a new type of ecological environment evaluation index named the remote sensing ecological index with local adaptability (RSEILA) to assess the eco-environment. The RSEILA represents an improvement of the remote sensing ecological index (RSEI) proposed in 2013. The RSEILA enhancement is mainly reflected in the correlation and spatial distribution characteristics between geographical elements. The results reveal that 1) the overall urban ecological environment in the Wuhan Urban Development Zone demonstrates a downward trend from 2013 to 2019, and the rate of decline during the period varies. 2) RSEILA decline is mainly found in the far suburbs, and ecological environment degradation mainly occurs due to the change in land-use type caused by the suburbanization process of urban expansion. 3) Because of the implementation of urban greening projects, the phenomenon of ecological environment optimization (green recovery) is observed in the central urban area of Wuhan. 4) Land use exhibits a notable correlation with the ecological environment, and different land-use types exhibit distinct degrees of ecological environment deterioration. The order of deterioration is: bare soil/sand > building > cropland > forests.

Eco-environmental assessment model of the mining area in Gongyi, China.

The ecological environment directly affects human life. One of the ecological environmental issues that China is presently facing is deterioration of the ecological environment due to mining. The pollution produced by mining causes the destruction of land, water bodies, the atmosphere, and vegetation resources and new geological problems that seriously impact human civilization and life. The main purpose of this study is to present an environmental assessment model of mine pollution to evaluate the eco-environment of mining. This study added mineral species and mining types into the factor layers and built an improved evaluation system to accurately evaluate the impact of mines on the eco-environment. In the non-mining area, the grades of the eco-environment were divided according to the Technical Criterion for Ecosystem Status Evaluation standard document. In the mining area, the grades of the assessment for the eco-environment were classified by a field survey. After comparing the accuracy of various methods, the support vector machine (SVM) model, with an accuracy of 94.8%, was chosen for the mining area, and the classification and regression tree (CART) model, with an accuracy of 89.36%, was chosen for the non-mining area. Finally, environmental assessment maps for the entire study area were generated. The results indicate that the mine environmental assessment system established by this study avoids the subjective limitations of traditional assessment methods, provides an effective method for assessing ecological quality, and will help relevant departments to plan for mine resources.

Spatial-temporal analysis and background value determination of molybdenum concentration in basins with high molybdenum geochemical background - A case study of the upper Yi River basin.

The environmental background value of the river section is important. It can be used to evaluate the effect of pollution control of the upstream of that river section, analyze the trend of environmental pollution, and assist the government to make decisions. Yi river is the main tributary of the Yellow River. In the headwaters of the Yi river, there are two very large molybdenum mines with a history of mining and smelting of many years. This area is also a region with a high molybdenum geochemical background. Using the collected regional molybdenum geochemical map, historical monitoring data, sampling data, remote sensing image, and spatial information of mineral enterprises, we found two reasons of why the molybdenum concentration is unusual in the basin. The first reason is the area is a high molybdenum region. The second reason is that the inherent solubility of molybdenum in the soil is changed due to human engineering activities. In this paper, we did a linear fitting on the soil samples and water samples collected from the natural areas and areas affected by human mining activities, and established a leaching model. By comparing the leaching capability of molybdenum in the soil of different areas, we found that the molybdenum release capability in areas affected by human mining was much higher than that in natural areas. Finally, this paper proposed a method to analyze the contribution rate of molybdenum concentration of this river section, using a combination of the leaching model and the D8 algorithm. The experimental results showed that the contribution rate of natural factors and human influence factors at the exit section of Yi River was 81.38% and 18.62% respectively. The background molybdenum concentration in this section was 0.16 mg/L.

A Fast Deploying Monitoring and Real-Time Early Warning System for the Baige Landslide in Tibet, China.

Landslide early warning systems (EWSs) have been widely used to reduce disaster losses. The effectiveness of a landslide EWS depends highly on the prediction methods, and it is difficult to correctly predict landslides in a timely manner. In this paper, we propose a real-time prediction method to provide real-time early warning of landslides by combining the Kalman filtering (KF), fast Fourier transform (FFT), and support vector machine (SVM) methods. We also designed a fast deploying monitoring system (FDMS) to monitor the displacement of landslides for real-time prediction. The FDMS can be quickly deployed compared to the existing system. This system also has high robustness due to the usage of the ad-hoc technique. The principle of this method is to extract the precursory features of the landslide from the surface displacement data obtained by the FDMS and, then, to train the KF-FFT-SVM model to make a prediction based on these precursory features. We applied this fast monitoring and real-time early warning system to the Baige landslide, Tibet, China. The results showed that the KF-FFT-SVM model was able to provide real-time early warning for the Baige landslide with high accuracy.

Monitoring the effects of open-pit mining on the eco-environment using a moving window-based remote sensing ecological index.

Environmental problems caused by mines have been increasing. As one of the most serious types of mining damage caused to the eco-environment, open pits have been the focus of monitoring and management. Previous studies have obtained effective results when evaluating the ecological quality of a mining area by using the remote sensing ecological index (RSEI). However, the calculation of RSEI does not consider that the ecological environmental impact is limited under natural conditions. To overcome this shortcoming, this paper proposes an improved RSEI based on a moving window model, namely the moving window-based remote sensing ecological index (MW-RSEI). This improved index is more in agreement with the First Law of Geography than RSEI. This study uses Landsat ETM/OLI/TIRS images to extract MW-RSEI information of a case area in Zhengzhou City, Henan Province, central China, in 2009 and 2018. The results revealed that the average value of MW-RSEI declined from 0.668 to 0.611 from 2009 to 2018, and the main drivers of the deterioration of the eco-environment were land use/cover (LUCC) changes, most of which were derived from urban expansion and mining. The serious impact of open pits on the eco-environment in mining areas is mainly due to their low vegetation cover; therefore, some effectively managed open pits can have a positive impact on the mining environment. The use of MW-RSEI provides valuable information on the eco-environment surrounding the open pit, which can be used for the rapid and effective monitoring of the eco-environment in mining areas.

Effect of Land Cover Fractions on Changes in Surface Urban Heat Islands Using Landsat Time-Series Images.

Man-made materials now cover a dominant proportion of urban areas, and such conditions not only change the absorption of solar radiation, but also the allocation of the solar radiation and cause the surface urban heat island effect, which is considered a serious problem associated with the deterioration of urban environments. Although numerous studies have been performed on surface urban heat islands, only a few have focused on the effect of land cover changes on surface urban heat islands over a long time period. Using six Landsat image scenes of the Metropolitan Development Area of Wuhan, our experiment (1) applied a mapping method for normalized land surface temperatures with three land cover fractions, which were impervious surfaces, non-chlorophyllous vegetation and soil and vegetation fractions, and (2) performed a fitting analysis of fierce change areas in the surface urban heat island intensity based on a time trajectory. Thematic thermal maps were drawn to analyze the distribution of and variations in the surface urban heat island in the study area. A Multiple Endmember Spectral Mixture Analysis was used to extract the land cover fraction information. Then, six ternary triangle contour graphics were drawn based on the land surface temperature and land cover fraction information. A time trajectory was created to summarize the changing characteristics of the surface urban heat island intensity. A fitting analysis was conducted for areas showing fierce changes in the urban heat intensity. Our results revealed that impervious surfaces had the largest impacts on surface urban heat island intensity, followed by the non-chlorophyllous vegetation and soil fraction. Moreover, the results indicated that the vegetation fraction can alleviate the occurrence of surface urban heat islands. These results reveal the impact of the land cover fractions on surface urban heat islands. Urban expansion generates impervious artificial objects that replace pervious natural objects, which causes an increase in land surface temperature and results in a surface urban heat island.

A Combination of Geographically Weighted Regression, Particle Swarm Optimization and Support Vector Machine for Landslide Susceptibility Mapping: A Case Study at Wanzhou in the Three Gorges Area, China.

In this study, a novel coupling model for landslide susceptibility mapping is presented. In practice, environmental factors may have different impacts at a local scale in study areas. To provide better predictions, a geographically weighted regression (GWR) technique is firstly used in our method to segment study areas into a series of prediction regions with appropriate sizes. Meanwhile, a support vector machine (SVM) classifier is exploited in each prediction region for landslide susceptibility mapping. To further improve the prediction performance, the particle swarm optimization (PSO) algorithm is used in the prediction regions to obtain optimal parameters for the SVM classifier. To evaluate the prediction performance of our model, several SVM-based prediction models are utilized for comparison on a study area of the Wanzhou district in the Three Gorges Reservoir. Experimental results, based on three objective quantitative measures and visual qualitative evaluation, indicate that our model can achieve better prediction accuracies and is more effective for landslide susceptibility mapping. For instance, our model can achieve an overall prediction accuracy of 91.10%, which is 7.8%-19.1% higher than the traditional SVM-based models. In addition, the obtained landslide susceptibility map by our model can demonstrate an intensive correlation between the classified very high-susceptibility zone and the previously investigated landslides.

Assessment of spatial distribution of soil loss over the upper basin of Miyun reservoir in China based on RS and GIS techniques.

Soil conservation planning often requires estimates of the spatial distribution of soil erosion at a catchment or regional scale. This paper applied the Revised Universal Soil Loss Equation (RUSLE) to investigate the spatial distribution of annual soil loss over the upper basin of Miyun reservoir in China. Among the soil erosion factors, which are rainfall erosivity (R), soil erodibility (K), slope length (L), slope steepness (S), vegetation cover (C), and support practice factor (P), the vegetative cover or C factor, which represents the effects of vegetation canopy and ground covers in reducing soil loss, has been one of the most difficult to estimate over broad geographic areas. In this paper, the C factor was estimated based on back propagation neural network and the results were compared with the values measured in the field. The correlation coefficient (r) obtained was 0.929. Then the C factor and the other factors were used as the input to RUSLE model. By integrating the six factor maps in geographical information system (GIS) through pixel-based computing, the spatial distribution of soil loss over the upper basin of Miyun reservoir was obtained. The results showed that the annual average soil loss for the upper basin of Miyun reservoir was 9.86 t ha(-1) ya(-1) in 2005, and the area of 46.61 km(2) (0.3%) experiences extremely severe erosion risk, which needs suitable conservation measures to be adopted on a priority basis. The spatial distribution of erosion risk classes was 66.9% very low, 21.89% low, 6.18% moderate, 2.89% severe, and 1.84% very severe. Thus, by using RUSLE in a GIS environment, the spatial distribution of water erosion can be obtained and the regions which susceptible to water erosion and need immediate soil conservation planning and application over the upper watershed of Miyun reservoir in China can be identified.

Spatial forecast of landslides in three gorges based on spatial data mining.

The Three Gorges is a region with a very high landslide distribution density and a concentrated population. In Three Gorges there are often landslide disasters, and the potential risk of landslides is tremendous. In this paper, focusing on Three Gorges, which has a complicated landform, spatial forecasting of landslides is studied by establishing 20 forecast factors (spectra, texture, vegetation coverage, water level of reservoir, slope structure, engineering rock group, elevation, slope, aspect, etc). China-Brazil Earth Resources Satellite (Cbers) images were adopted based on C4.5 decision tree to mine spatial forecast landslide criteria in Guojiaba Town (Zhigui County) in Three Gorges and based on this knowledge, perform intelligent spatial landslide forecasts for Guojiaba Town. All landslides lie in the dangerous and unstable regions, so the forecast result is good. The method proposed in the paper is compared with seven other methods: IsoData, K-Means, Mahalanobis Distance, Maximum Likelihood, Minimum Distance, Parallelepiped and Information Content Model. The experimental results show that the method proposed in this paper has a high forecast precision, noticeably higher than that of the other seven methods.